Comparer des méthodes

	Modèle ARCH (Hétéroscédasticité Conditionnelle Autorégressive)	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1982	1970
Auteur d'origine	Robert F. Engle	George Box and Gwilym Jenkins
Type	Conditional volatility model	Time series forecasting model
Source fondatrice	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Apparentées	6	6
Résumé	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.